{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generation of the eCDFs and manual annotations figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T09:30:53.484149Z",
     "start_time": "2020-06-10T09:30:51.560487Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:41:22.893020Z",
     "start_time": "2020-06-10T13:41:22.801744Z"
    }
   },
   "outputs": [],
   "source": [
    "# Naming system\n",
    "# x[method][12|21][c?]\n",
    "#   ^       ^      ^-> if c appears it was trained with cosine, mse otherwise\n",
    "#   |       +--------> 12 if the registration is done from mod. A to B, 21 if B to A\n",
    "#   +----------------> method name\n",
    "\n",
    "df = pd.read_csv(\"results/CurveAlign.csv\").sort_values(by=\"Filename\")\n",
    "xe = df.Error\n",
    "\n",
    "# ~~~ MSE PART ~~~\n",
    "# Alpha-AMD (Not multistart)\n",
    "df = pd.read_csv(\"results/AlphaAMD.csv\").sort_values(by=\"Index\")\n",
    "xa12 = df[df.IsRegisteredAtoB == 1].Error\n",
    "xa21 = df[df.IsRegisteredAtoB == 0].Error\n",
    "# Alpha-AMD (Multistart)\n",
    "df = pd.read_csv(\"results/AlphaAMD_MS.csv\").sort_values(by=\"Index\")\n",
    "xm12 = df[df.IsRegisteredAtoB == 1].Error\n",
    "xm21 = df[df.IsRegisteredAtoB == 0].Error\n",
    "# SIFT\n",
    "xs12 = pd.read_csv(\"results/SIFT_AtoB.csv\").sort_values(by=\"Filename\").Error\n",
    "xs21 = pd.read_csv(\"results/SIFT_BtoA.csv\").sort_values(by=\"Filename\").Error\n",
    "# Mutual Information (Original)\n",
    "xmio12 = pd.read_csv(\"results/MI_original_BFT_SHGR.csv\").sort_values(by=\"Filename\").Error\n",
    "xmio21 = pd.read_csv(\"results/MI_original_SHGT_BFR.csv\").sort_values(by=\"Filename\").Error\n",
    "# Mutual Infoormation (Latent spaces)\n",
    "xmil12 = pd.read_csv(\"results/MI_CoMIR_AtoB.csv\").sort_values(by=\"Filename\").Error\n",
    "xmil21 = pd.read_csv(\"results/MI_CoMIR_BtoA.csv\").sort_values(by=\"Filename\").Error\n",
    "\n",
    "# ~~~ COSINE PART ~~~\n",
    "# Alpha-AMD (Not multistart)\n",
    "df = pd.read_csv(\"results/cosine_AlphaAMD.csv\").sort_values(by=\"Index\")\n",
    "xa12c = df[df.IsRegisteredAtoB == 1].Error\n",
    "xa21c = df[df.IsRegisteredAtoB == 0].Error\n",
    "# Alpha-AMD (Multistart)\n",
    "df = pd.read_csv(\"results/cosine_AlphaAMD_MS.csv\").sort_values(by=\"Index\")\n",
    "xm12c = df[df.IsRegisteredAtoB == 1].Error\n",
    "xm21c = df[df.IsRegisteredAtoB == 0].Error\n",
    "# SIFT\n",
    "xs12c = pd.read_csv(\"results/cosine_SIFT_AtoB.csv\").sort_values(by=\"Filename\").Error\n",
    "xs21c = pd.read_csv(\"results/cosine_SIFT_BtoA.csv\").sort_values(by=\"Filename\").Error\n",
    "# Mutual Infoormation (Latent spaces)\n",
    "xmil12c = pd.read_csv(\"results/cosine_MI_CoMIR_AtoB.csv\", sep=\",\").sort_values(by=\"Filename\").Error\n",
    "xmil21c = pd.read_csv(\"results/cosine_MI_CoMIR_BtoA.csv\", sep=\",\").sort_values(by=\"Filename\").Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:35:19.240524Z",
     "start_time": "2020-06-10T13:35:19.169847Z"
    }
   },
   "outputs": [],
   "source": [
    "xh = np.array([\n",
    "    pd.read_csv(f\"results/Manual_{i}.csv\").Error.dropna() for i in range(6)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:35:19.922070Z",
     "start_time": "2020-06-10T13:35:19.899530Z"
    }
   },
   "outputs": [],
   "source": [
    "from collections import namedtuple\n",
    "import warnings\n",
    "\n",
    "WilcoxonResult = namedtuple('WilcoxonResult', ('statistic', 'pvalue'))\n",
    "def wilcoxon(x, y=None, zero_method=\"wilcox\", correction=False,\n",
    "             alternative=\"two-sided\"):\n",
    "    if zero_method not in [\"wilcox\", \"pratt\", \"zsplit\"]:\n",
    "        raise ValueError(\"Zero method should be either 'wilcox' \"\n",
    "                         \"or 'pratt' or 'zsplit'\")\n",
    "\n",
    "    if alternative not in [\"two-sided\", \"less\", \"greater\"]:\n",
    "        raise ValueError(\"Alternative must be either 'two-sided', \"\n",
    "                         \"'greater' or 'less'\")\n",
    "\n",
    "    if y is None:\n",
    "        d = np.asarray(x)\n",
    "        if d.ndim > 1:\n",
    "            raise ValueError('Sample x must be one-dimensional.')\n",
    "    else:\n",
    "        x, y = map(np.asarray, (x, y))\n",
    "        if x.ndim > 1 or y.ndim > 1:\n",
    "            raise ValueError('Samples x and y must be one-dimensional.')\n",
    "        if len(x) != len(y):\n",
    "            raise ValueError('The samples x and y must have the same length.')\n",
    "        d = x - y\n",
    "\n",
    "    if zero_method in [\"wilcox\", \"pratt\"]:\n",
    "        n_zero = np.sum(d == 0, axis=0)\n",
    "        if n_zero == len(d):\n",
    "            raise ValueError(\"zero_method 'wilcox' and 'pratt' do not work if \"\n",
    "                             \"the x - y is zero for all elements.\")\n",
    "\n",
    "    if zero_method == \"wilcox\":\n",
    "        # Keep all non-zero differences\n",
    "        d = np.compress(np.not_equal(d, 0), d, axis=-1)\n",
    "\n",
    "    count = len(d)\n",
    "    if count < 10:\n",
    "        warnings.warn(\"Sample size too small for normal approximation.\")\n",
    "\n",
    "    r = st.rankdata(abs(d))\n",
    "    r_plus = np.sum((d > 0) * r, axis=0)\n",
    "    r_minus = np.sum((d < 0) * r, axis=0)\n",
    "    W = r_plus - r_minus\n",
    "\n",
    "    if zero_method == \"zsplit\":\n",
    "        r_zero = np.sum((d == 0) * r, axis=0)\n",
    "        r_plus += r_zero / 2.\n",
    "        r_minus += r_zero / 2.\n",
    "\n",
    "    # return min for two-sided test, but r_plus for one-sided test\n",
    "    # the literature is not consistent here\n",
    "    # r_plus is more informative since r_plus + r_minus = count*(count+1)/2,\n",
    "    # i.e. the sum of the ranks, so r_minus and the min can be inferred\n",
    "    # (If alternative='pratt', r_plus + r_minus = count*(count+1)/2 - r_zero.)\n",
    "    # [3] uses the r_plus for the one-sided test, keep min for two-sided test\n",
    "    # to keep backwards compatibility\n",
    "    if alternative == \"two-sided\":\n",
    "        T = min(r_plus, r_minus)\n",
    "    else:\n",
    "        T = r_plus\n",
    "    mn = count * (count + 1.) * 0.25\n",
    "    se = count * (count + 1.) * (2. * count + 1.)\n",
    "\n",
    "    if zero_method == \"pratt\":\n",
    "        r = r[d != 0]\n",
    "        # normal approximation needs to be adjusted, see Cureton (1967)\n",
    "        mn -= n_zero * (n_zero + 1.) * 0.25\n",
    "        se -= n_zero * (n_zero + 1.) * (2. * n_zero + 1.)\n",
    "\n",
    "    replist, repnum = st.find_repeats(r)\n",
    "    if repnum.size != 0:\n",
    "        # Correction for repeated elements.\n",
    "        se -= 0.5 * (repnum * (repnum * repnum - 1)).sum()\n",
    "\n",
    "    se = np.sqrt(se / 24)\n",
    "\n",
    "    # apply continuity correction if applicable\n",
    "    d = 0\n",
    "    if correction:\n",
    "        if alternative == \"two-sided\":\n",
    "            d = 0.5 * np.sign(T - mn)\n",
    "        elif alternative == \"less\":\n",
    "            d = -0.5\n",
    "        else:\n",
    "            d = 0.5\n",
    "\n",
    "    # compute statistic and p-value using normal approximation\n",
    "    z = (T - mn - d) / se\n",
    "    if alternative == \"two-sided\":\n",
    "        prob = 2. * st.distributions.norm.sf(abs(z))\n",
    "    elif alternative == \"greater\":\n",
    "        # large T = r_plus indicates x is greater than y; i.e.\n",
    "        # accept alternative in that case and return small p-value (sf)\n",
    "        prob = st.distributions.norm.sf(z)\n",
    "    else:\n",
    "        prob = st.distributions.norm.cdf(z)\n",
    "    direction = 2 * (r_plus < r_minus) - 1\n",
    "    return W, prob, direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:39:20.780694Z",
     "start_time": "2020-06-10T13:39:16.663211Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.5472000344575845\n",
      "p-value: 4.9809900969124296e-11\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as st\n",
    "import scipy.special as sp\n",
    "import matplotlib.ticker as mtick\n",
    "import matplotlib as mpl\n",
    "from cycler import cycler\n",
    "plt.rcParams.update({'font.size': 13})\n",
    "\n",
    "def plot_cdf(x, bins, cumulative=True, bands=True, confidence=0.95, **kwargs):\n",
    "    hist, bin_edges = np.histogram(x, bins=bins)\n",
    "    N = len(x)\n",
    "    # We need to pad because plt.step(where=\"mid\") has half steps\n",
    "    # in the beginning and in the end. (purely graphical purpose)\n",
    "    hist = np.pad(np.cumsum(hist), 1, mode=\"edge\")\n",
    "    p = hist / N\n",
    "    # plotting\n",
    "    t = np.concatenate([\n",
    "        bins[0:1],\n",
    "        (bins[:-1] + bins[1:]) / 2,\n",
    "        bins[-1:]\n",
    "    ])\n",
    "    plt.step(t, p, where=\"mid\", **kwargs)\n",
    "\n",
    "    # Confidence bands\n",
    "    # Binomial (Clopper-Pearson)\n",
    "    alpha = 1. - confidence\n",
    "    z = 1 - alpha/2.\n",
    "    errl = st.beta.ppf(alpha/2, hist, N-hist+1)\n",
    "    errh = st.beta.ppf(1-alpha/2, hist+1, N-hist)\n",
    "    errl[hist == 0], errh[hist == 0] = 0, 1 - (alpha/2)**(1/N)\n",
    "    errl[hist == N], errh[hist == N] = (alpha/2)**(1/N), 1\n",
    "\n",
    "    opacity = 0.15 if bands else 0.0\n",
    "    plt.fill_between(t,\n",
    "        errl, errh,\n",
    "        interpolate=False, step=\"mid\", alpha=opacity\n",
    "    )\n",
    "    return hist / N\n",
    "\n",
    "def pvalue_ubound(pvalue, d=4):\n",
    "    return np.ceil(pvalue * 10**d) / 10**d\n",
    "\n",
    "def pstar(p, latex=False):\n",
    "    b = [0, 1e-3, 1e-2, 5e-2, 1e-1, 1e0]\n",
    "    s = [\"***\", \"**\", \"*\", \"+\", \"\"]\n",
    "    idx = np.digitize(p, b).item() - 1\n",
    "    if latex:\n",
    "        return \"\\\\!\".join(s[idx])\n",
    "    return s[idx]\n",
    "\n",
    "def annotate_pvalue(xpos, p1, p2, x1, x2, margin=16, margin2=4, stars=True):\n",
    "    yl, yh = p1[-1], p2[-1]\n",
    "    ym = (yh + yl)/2\n",
    "    # Plotting the bracket\n",
    "    plt.hlines([yl, yh], xpos, xpos+margin, zorder=3, clip_on=False)\n",
    "    plt.vlines(xpos+margin, yl, yh, zorder=3, clip_on=False)\n",
    "    plt.hlines(ym, xpos+margin, xpos+margin+margin2, zorder=3, clip_on=False)\n",
    "    statistic, pvalue, direction = wilcoxon(\n",
    "        np.clip(x1, 0, xpos),\n",
    "        np.clip(x2, 0, xpos)\n",
    "    )\n",
    "    print(\"p-value:\", pvalue)\n",
    "    # Plotting the p-value\n",
    "    pvalue= pvalue_ubound(pvalue, 4)\n",
    "    ptext = \"$p<\" + f\"{pvalue:.4f}\".lstrip(\"0\")\n",
    "    if stars:\n",
    "        ptext += f\"~{pstar(pvalue, latex=True)}$\"\n",
    "    else:\n",
    "        ptext += \"$\"\n",
    "    plt.text(xpos+margin+margin2+1, ym, ptext, zorder=3, rotation=\"vertical\", va=\"center\")\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "mpl.rcParams['axes.prop_cycle'] = cycler(color=plt.cm.tab20.colors)\n",
    "plt.grid()\n",
    "\n",
    "# EMPIRICAL CUMULATIVE DISTRIBUTION FUNCTIONS\n",
    "bins = np.logspace(0., 2., 1000)\n",
    "p1 = plot_cdf(xa12, bins, bands=False, confidence=0.95, color=\"C0\", label=\"$\\\\alpha_{AMD} ~ A \\\\to B$ (CoMIR)\")\n",
    "p2 = plot_cdf(xa21, bins, bands=False, confidence=0.95, color=\"C1\", label=\"$\\\\alpha_{AMD} ~ B \\\\to A$ (CoMIR)\")\n",
    "p3 = plot_cdf(xmio12, bins, bands=False, confidence=0.95, color=\"C8\", label=\"MI (Original)\")\n",
    "p4 = plot_cdf(xm12, bins, bands=False, confidence=0.95, color=\"C2\", label=\"$\\\\alpha_{AMD} (MS) ~ A \\\\to B$ (CoMIR)\")\n",
    "p5 = plot_cdf(xm21, bins, bands=False, confidence=0.95, color=\"C3\", label=\"$\\\\alpha_{AMD} (MS) ~ B \\\\to A$ (CoMIR)\")\n",
    "p6 = plot_cdf(xe, bins, bands=False, confidence=0.95, color=\"C6\", label=\"CurveAlign (Original)\")\n",
    "p7 = plot_cdf(xs12, bins, bands=False, confidence=0.95, color=\"C4\", label=\"SIFT $A \\\\to B$ (CoMIR)\")\n",
    "p8 = plot_cdf(xs21, bins, bands=False, confidence=0.95, color=\"C5\", label=\"SIFT $B \\\\to A$ (CoMIR)\")\n",
    "p9 = plot_cdf(np.median(xh, axis=0), bins, bands=False, confidence=0.95, color=\"black\", ls=\":\", label=\"Median Manual Reg. (Original)\")\n",
    "\n",
    "# WILCOXON PAIRED TEST (P-VALUES)\n",
    "xpos = bins[-1]\n",
    "m = 6\n",
    "annotate_pvalue(xpos, p4, p7, xm21.to_numpy(), xs12.to_numpy(), margin=m)\n",
    "annotate_pvalue(xpos, p7, p6, xs12.to_numpy(), xe.to_numpy(), margin=m, margin2=m+10)\n",
    "\n",
    "#plt.legend(bbox_to_anchor=(0, -0.4, 1, 0.2), loc=8, ncol=3)\n",
    "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3)\n",
    "ax = plt.gca()\n",
    "# Main axis\n",
    "# y axis\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0, decimals=0))\n",
    "ax.set_ylabel(\"Cumulative success rate $\\widehat{F}_n(x)$\")\n",
    "# x axis\n",
    "ax.set_xscale(\"log\")\n",
    "ax.xaxis.set_major_formatter(mtick.ScalarFormatter())\n",
    "ax.set_xlabel(\"Absolute error [px]\")\n",
    "xticks = np.array([1, 2, 5, 10, 20, 30, 40, 60, 80, 100])\n",
    "ax.set_xticks(xticks)\n",
    "ax.set_xlim(bins[0], bins[-1])\n",
    "ax.set_ylim(0, 1)\n",
    "\n",
    "#''' Secondary axis (x axis)\n",
    "imwidth = 784\n",
    "ax2 = ax.twiny()\n",
    "ax2.set_xlabel(\"Relative error [%]\")\n",
    "ax2.set_xscale(\"log\")\n",
    "ax2.xaxis.set_major_formatter(mtick.PercentFormatter(1.0, decimals=1))\n",
    "xticks = np.array([0.2, 0.5, 1, 2, 4, 6, 10]) / 100\n",
    "ax2.set_xticks(xticks)\n",
    "ax2.set_xlim(bins[0]/imwidth, bins[-1]/imwidth)\n",
    "#'''\n",
    "\n",
    "#plt.title(\"Empirical CDF\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"base_methods_cdf_wide.pdf\", bbox_inches='tight', pad_inches=0.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:42:06.525924Z",
     "start_time": "2020-06-10T13:41:52.673707Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as st\n",
    "import scipy.special as sp\n",
    "import matplotlib.ticker as mtick\n",
    "import matplotlib as mpl\n",
    "from cycler import cycler\n",
    "plt.rcParams.update({'font.size': 13})\n",
    "\n",
    "def plot_cdf(ax, x, bins, cumulative=True, bands=True, confidence=0.95, **kwargs):\n",
    "    hist, bin_edges = np.histogram(x, bins=bins)\n",
    "    N = len(x)\n",
    "    # We need to pad because plt.step(where=\"mid\") has half steps\n",
    "    # in the beginning and in the end. (purely graphical purpose)\n",
    "    hist = np.pad(np.cumsum(hist), 1, mode=\"edge\")\n",
    "    p = hist / N\n",
    "    # plotting\n",
    "    t = np.concatenate([\n",
    "        bins[0:1],\n",
    "        (bins[:-1] + bins[1:]) / 2,\n",
    "        bins[-1:]\n",
    "    ])\n",
    "    ax.step(t, p, where=\"mid\", **kwargs)\n",
    "    return hist / N\n",
    "    # Confidence bands\n",
    "    # Binomial (Clopper-Pearson)\n",
    "    alpha = 1. - confidence\n",
    "    z = 1 - alpha/2.\n",
    "    errl = st.beta.ppf(alpha/2, hist, N-hist+1)\n",
    "    errh = st.beta.ppf(1-alpha/2, hist+1, N-hist)\n",
    "    errl[hist == 0], errh[hist == 0] = 0, 1 - (alpha/2)**(1/N)\n",
    "    errl[hist == N], errh[hist == N] = (alpha/2)**(1/N), 1\n",
    "\n",
    "    opacity = 0.15 if bands else 0.0\n",
    "    fig.fill_between(t,\n",
    "        errl, errh,\n",
    "        interpolate=False, step=\"mid\", alpha=opacity\n",
    "    )\n",
    "    return hist / N\n",
    "\n",
    "def pvalue_ubound(pvalue, d=4):\n",
    "    return np.ceil(pvalue * 10**d) / 10**d\n",
    "\n",
    "def pstar(p, latex=False):\n",
    "    b = [0, 1e-3, 1e-2, 5e-2, 1e-1, 1e0]\n",
    "    s = [\"***\", \"**\", \"*\", \"+\", \"\"]\n",
    "    idx = np.digitize(p, b).item() - 1\n",
    "    if latex:\n",
    "        return \"\\\\!\".join(s[idx])\n",
    "    return s[idx]\n",
    "\n",
    "def annotate_pvalue(ax, xpos, p1, p2, x1, x2, margin=16, margin2=4, stars=True):\n",
    "    yl, yh = p1[-1], p2[-1]\n",
    "    ym = (yh + yl)/2\n",
    "    # Plotting the bracket\n",
    "    ax.hlines([yl, yh], xpos, xpos+margin, zorder=3, clip_on=False)\n",
    "    ax.vlines(xpos+margin, yl, yh, zorder=3, clip_on=False)\n",
    "    ax.hlines(ym, xpos+margin, xpos+margin+margin2, zorder=3, clip_on=False)\n",
    "    W, pvalue, direction = wilcoxon(\n",
    "        np.clip(x1, 0, xpos),\n",
    "        np.clip(x2, 0, xpos)\n",
    "    )\n",
    "    # Plotting the direction of the rejection\n",
    "    #ptext = \"$>$\" if -direction > 0 else \"$<$\"\n",
    "    #ax.text(xpos+margin/2, ym, ptext, zorder=3, rotation=\"vertical\", ha=\"center\", va=\"center\")\n",
    "    # Plotting the p-value\n",
    "    pvalue= pvalue_ubound(pvalue, 4)\n",
    "    ptext = \"$p<\" + f\"{pvalue:.4f}\".lstrip(\"0\")\n",
    "    if stars:\n",
    "        ptext += f\"~{pstar(pvalue, latex=True)}$\"\n",
    "    else:\n",
    "        ptext += \"$\"\n",
    "    S = len(x1) * (len(x1) + 1) / 2\n",
    "    ptext += f\"\\n$r={W/S:.4f}$\"\n",
    "    ax.text(xpos+margin+margin2+1, ym, ptext, zorder=3, rotation=\"vertical\", va=\"center\")\n",
    "\n",
    "fig, ax = plt.subplots(2, 2, figsize=(14, 12))\n",
    "ax = ax.ravel()\n",
    "mpl.rcParams['axes.prop_cycle'] = cycler(color=plt.cm.tab20.colors)\n",
    "\n",
    "# EMPIRICAL CUMULATIVE DISTRIBUTION FUNCTIONS\n",
    "bins = np.logspace(0., 2., 1000)\n",
    "p1  = plot_cdf(ax[0], xa12, bins, bands=False, confidence=0.95)\n",
    "p2  = plot_cdf(ax[0], xa21, bins, bands=False, confidence=0.95)\n",
    "p3  = plot_cdf(ax[0], xa12c, bins, bands=False, confidence=0.95)\n",
    "p4  = plot_cdf(ax[0], xa21c, bins, bands=False, confidence=0.95)\n",
    "p5  = plot_cdf(ax[1], xm12, bins, bands=False, confidence=0.95)\n",
    "p6  = plot_cdf(ax[1], xm21, bins, bands=False, confidence=0.95)\n",
    "p7  = plot_cdf(ax[1], xm12c, bins, bands=False, confidence=0.95)\n",
    "p8  = plot_cdf(ax[1], xm21c, bins, bands=False, confidence=0.95)\n",
    "p9  = plot_cdf(ax[2], xs12, bins, bands=False, confidence=0.95)\n",
    "p10 = plot_cdf(ax[2], xs21, bins, bands=False, confidence=0.95)\n",
    "p11 = plot_cdf(ax[2], xs12c, bins, bands=False, confidence=0.95)\n",
    "p12 = plot_cdf(ax[2], xs21c, bins, bands=False, confidence=0.95)\n",
    "p13 = plot_cdf(ax[3], xmil12, bins, bands=False, confidence=0.95, label=\"MSE $A \\\\to B$\")\n",
    "p14 = plot_cdf(ax[3], xmil21, bins, bands=False, confidence=0.95, label=\"MSE $B \\\\to A$\")\n",
    "p15 = plot_cdf(ax[3], xmil12c, bins, bands=False, confidence=0.95, label=\"Cosine $A \\\\to B$\")\n",
    "p16 = plot_cdf(ax[3], xmil21c, bins, bands=False, confidence=0.95, label=\"Cosine $B \\\\to A$\")\n",
    "\n",
    "# WILCOXON PAIRED TEST (P-VALUES)\n",
    "xpos = bins[-1]\n",
    "m = 8\n",
    "annotate_pvalue(ax[0], xpos,  p1,  p3, xa12.to_numpy(), xa12c.to_numpy(), margin=m, margin2=2*m)\n",
    "annotate_pvalue(ax[1], xpos,  p5,  p7, xm12.to_numpy(), xm12c.to_numpy(), margin=m, margin2=2*m)\n",
    "annotate_pvalue(ax[2], xpos,  p9, p11, xs12.to_numpy(), xs12c.to_numpy(), margin=m, margin2=2*m)\n",
    "annotate_pvalue(ax[3], xpos, p13, p15, xmil12.to_numpy(), xmil12c.to_numpy(), margin=m, margin2=2*m)\n",
    "\n",
    "methods = [\"$\\\\alpha_{AMD}$\", \"$\\\\alpha_{AMD}$ (MS)\", \"SIFT\", \"Mutual Information (MI)\"]\n",
    "letters = [\"a\", \"b\", \"c\", \"d\"]\n",
    "for i, axe in enumerate(ax):\n",
    "    axe.grid()\n",
    "    # Main axis\n",
    "    axe.annotate(methods[i], (1.2, .90), fontsize=28, alpha=0.4, zorder=3)\n",
    "    # y axis\n",
    "    axe.yaxis.set_major_formatter(mtick.PercentFormatter(1.0, decimals=0))\n",
    "    axe.set_ylabel(\"Cumulative success rate $\\widehat{F}_n(x)$\")\n",
    "    # x axis\n",
    "    axe.set_xscale(\"log\")\n",
    "    axe.xaxis.set_major_formatter(mtick.ScalarFormatter())\n",
    "    axe.set_xlabel(f\"Absolute error [px]\\n({letters[i]})\")\n",
    "    xticks = np.array([1, 2, 5, 10, 20, 30, 40, 60, 80, 100])\n",
    "    axe.set_xticks(xticks)\n",
    "    axe.set_xlim(bins[0], bins[-1])\n",
    "    axe.set_ylim(0, 1)\n",
    "\n",
    "    #''' Secondary axis (x axis)\n",
    "    imwidth = 784\n",
    "    ax2 = axe.twiny()\n",
    "    ax2.set_xlabel(\"Relative error [%]\")\n",
    "    ax2.set_xscale(\"log\")\n",
    "    ax2.xaxis.set_major_formatter(mtick.PercentFormatter(1.0, decimals=1))\n",
    "    xticks = np.array([0.2, 0.5, 1, 2, 4, 6, 10]) / 100\n",
    "    ax2.set_xticks(xticks)\n",
    "    ax2.set_xlim(bins[0]/imwidth, bins[-1]/imwidth)\n",
    "    #'''\n",
    "\n",
    "handles, labels = ax[-1].get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(0.5, 1.02), loc='upper center', ncol=4)\n",
    "\n",
    "#plt.title(\"Empirical CDF\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"base_methods_cdf_mse_vs_cosine.pdf\", bbox_inches='tight', pad_inches=0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:50:17.008762Z",
     "start_time": "2020-06-10T13:50:16.207931Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = pd.read_csv(f\"results/Manual_0.csv\").Error.dropna(axis=0).index\n",
    "N = len(idx)\n",
    "\n",
    "def G(n):\n",
    "    # Gamma correction of the Rayleigh parameter estimator\n",
    "    return np.exp(sp.loggamma(n) - sp.loggamma(n + 0.5) + .5 * np.log(n))\n",
    "\n",
    "sigmas = []\n",
    "for i in range(N):\n",
    "    mask = xh[:, i] < 100\n",
    "    sigmas.append(G(mask.sum()) * np.sqrt(0.5*np.mean(xh[mask, i]**2, axis=0)))\n",
    "sigmas = np.array(sigmas)\n",
    "\n",
    "plt.figure(figsize=(7, 5))\n",
    "t = np.arange(10)+1\n",
    "plt.xticks(t)\n",
    "for i in range(6):\n",
    "    plt.scatter(t, xh[i], c=\"C0\", zorder=3)\n",
    "for i in range(10):\n",
    "    p = xm12.iloc[idx].to_numpy()\n",
    "    if p[i] < 100:\n",
    "        plt.scatter(t[i], p[i], marker=\"d\", zorder=4, facecolors='none', edgecolors='C2')\n",
    "for i in range(10):\n",
    "    p = xs12.iloc[idx].to_numpy()\n",
    "    if p[i] < 100:\n",
    "        plt.scatter(t[i], p[i], marker=\"o\", zorder=4, facecolors='none', edgecolors='C4')\n",
    "plt.hlines(sigmas, t-.4, t+.4, color=\"C0\", label=\"Deviation of error $\\hat \\sigma$\", alpha=0.7)\n",
    "Ks = np.sum(xh < 100, axis=0)\n",
    "lo, hi = np.array(st.chi.interval(.95, df=2*Ks))\n",
    "lo *= sigmas / np.sqrt(2*Ks)\n",
    "hi *= sigmas / np.sqrt(2*Ks)\n",
    "plt.bar(t, bottom=lo, height=hi-lo, color=\"C1\", alpha=0.3, label=\"95% C.I. of $\\hat \\sigma$\")\n",
    "\n",
    "plt.scatter([], [], c=\"C0\", label=\"Individual error\")\n",
    "plt.scatter([], [], marker=\"d\", label=\"$\\\\alpha_{AMD} (MS) ~ A \\\\to B$ error\", facecolors='none', edgecolors='C2')\n",
    "plt.scatter([], [], marker=\"o\", label=\"$SIFT ~ A \\\\to B$ error\", facecolors='none', edgecolors='C4')\n",
    "\n",
    "plt.grid(axis=\"x\")\n",
    "#plt.gca().set_xticklabels(imglabels, rotation=45)\n",
    "plt.gca().set_xticklabels([f\"Image {i+1}\" for i in range(N)], rotation=45, ha=\"right\")\n",
    "#plt.xlabel(\"Images\")\n",
    "plt.ylabel(\"Manual alignment error [px]\")\n",
    "plt.ylim(0, 100)\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "plt.legend(np.array(handles)[[1, 0, 3, 2]], np.array(labels)[[1, 0, 3, 2]])\n",
    "plt.savefig(\"manual_annotations.pdf\", bbox_inches='tight', pad_inches=0.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
